Tunnel Fissure Water Structure Detection Based on Multipoint Geostatistical Bayesian Method

Nuclear magnetic resonance (NMR) has various applications in underground engineering, particularly in tunnels, where it plays a crucial role in the early detection of potential disaster-inducing water sources. Water bodies that pose a threat in tunnels often manifest as fissure water nestled between rock masses. However, the current application of tunnel NMR detection is limited to the one-dimensional (1-D) layered models, specifically magnetic resonance sounding (MRS), which fails to capture the spatial structure of such fissure water. To precisely locate disaster-inducing water bodies, the MRS method needs to be extended to two-dimensional (2-D) structural detection. Thus, we propose a method for imaging the structure of tunnel fissure water based on multipoint geostatistical Bayesian framework. This method relies solely on data acquired from 1-D MRS measurements, complemented by borehole data obtained during the construction process, to detect 2-D structures of fissure water, without the need of complicated 2-D measurement process. Furthermore, we enhance the Markov chain Monte Carlo (MCMC) algorithm to improve the sampling performance of Bayesian method through a biased-estimation constraint strategy. Through synthetic experiments, we demonstrate the effectiveness of this method in predicting the intricate distribution of fissure water and its interconnected structure. Practical validation is provided on the basis of a series of field-measured data collected in an actual tunnel.